title: Data-based filtering for replicated high-throughput transcriptome sequencing experiments.
abstract: BACKGROUND
  RNA sequencing is now widely performed to study differential expression among experimental conditions. As tests are performed on a large number of genes, stringent false-discovery rate control is required at the expense of detection power. Ad hoc filtering techniques are regularly used to moderate this correction by removing genes with low signal, with little attention paid to their impact on downstream analyses.
  
  
  RESULTS
  We propose a data-driven method based on the Jaccard similarity index to calculate a filtering threshold for replicated RNA sequencing data. In comparisons with alternative data filters regularly used in practice, we demonstrate the effectiveness of our proposed method to correctly filter lowly expressed genes, leading to increased detection power for moderately to highly expressed genes. Interestingly, this data-driven threshold varies among experiments, highlighting the interest of the method proposed here.
  
  
  BACKGROUND
  The proposed filtering method is implemented in the R package HTSFilter available on Bioconductor.
authors.affiliation: INRA, UMR1313 Génétique animale et biologie intégrative, 78352 Jouy-en-Josas, France. andrea.rau@jouy.inra.fr
authors.forename: Andrea
authors.initials: A
authors.lastname: Rau
authors.forename: Mélina
authors.initials: M
authors.lastname: Gallopin
authors.forename: Gilles
authors.initials: G
authors.lastname: Celeux
authors.forename: Florence
authors.initials: F
authors.lastname: Jaffrézic
mesh_terms: D000818:Animals; D020869:Gene Expression Profiling / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D051379:Mice; D017423:Sequence Analysis, RNA / Q000379:methods*